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Liver fat surrogates: HSI and NAFLD Liver Fat Score

Source: R/liver_fat_markers.R
liver_fat_markers.Rd

Computes:

  • HSI = 8 * (ALT/AST) + BMI + 2 (if female) + 2 (if diabetes)

  • NAFLD-LFS = -2.89 + 1.18MetS + 0.45Type2DM + 0.15Insulin_u + 0.04AST - 0.94*(AST/ALT)

Usage

liver_fat_markers(
  data,
  col_map = list(ALT = "ALT", AST = "AST", BMI = "BMI", sex = "sex", diabetes =
    "diabetes", MetS = "MetS", insulin = "insulin", I0 = "I0", waist = "waist", TG =
    "TG", HDL_c = "HDL_c", sbp = "sbp", bp_sys = "bp_sys", bp_treated = "bp_treated",
    glucose = "glucose", G0 = "G0"),
  na_action = c("keep", "omit", "error", "ignore", "warn"),
  na_warn_prop = 0.2,
  check_extreme = FALSE,
  extreme_action = c("warn", "cap", "error", "ignore", "NA"),
  extreme_rules = NULL,
  verbose = FALSE
)

Arguments

data

Data frame with needed columns (see col_map).

col_map

Named list mapping:

  • Required for HSI: ALT, AST, BMI

  • Optional direct inputs: sex, diabetes, MetS, insulin

  • Optional to derive MetS or insulin: I0, waist, TG, HDL_c, sbp, bp_sys, bp_treated, glucose, G0

na_action

One of c("keep","omit","error","ignore","warn").

na_warn_prop

Proportion in \([0,1]\) for high-missingness warnings when na_action = "warn". Default 0.2.

check_extreme

Logical; if TRUE, scan selected inputs for plausible ranges.

extreme_action

One of c("warn","cap","error","ignore","NA") controlling how extremes are handled.

extreme_rules

Optional named list of c(min,max) to override defaults.

verbose

Logical; if TRUE, prints progress.

Value

A tibble with columns HSI and NAFLD_LFS.

Details

Assumptions/units:

  • ALT, AST in U/L; BMI in kg/m^2; I0 in pmol/L (converted to muU/mL via /6).

  • MetS is taken directly if provided; otherwise derived via NCEP-ATP III when sufficient inputs exist.

  • Type2DM is taken from diabetes (logical or 0/1).

References

Lee J, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, Kim YJ, Yoon J, Cho S, Sung M, Lee H (2010). “Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease.” Digestive and Liver Disease, 42(7), 503–508. doi:10.1016/j.dld.2009.08.002 . Kotronen A, Peltonen M, Hakkarainen A, Sevastianova K, Bergholm R, Johansson LM, Lundbom N, Rissanen A, Ridderstrale M, Groop L, Orho-Melander M, Yki-Järvinen H (2009). “Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors.” Gastroenterology, 137(3), 865–872. doi:10.1053/j.gastro.2009.06.005 .

Examples

df <- data.frame(ALT=20, AST=25, BMI=27, sex="female", diabetes=FALSE, I0=60)
liver_fat_markers(
  df,
  col_map = list(ALT="ALT", AST="AST", BMI="BMI",
                 sex="sex", diabetes="diabetes", I0="I0")
)
#> # A tibble: 1 × 2
#>     HSI NAFLD_LFS
#>   <dbl>     <dbl>
#> 1  35.4        NA